Knowledge Discovery strategy over patient performance data towards the extraction of hemiparesis-inherent features: A case study

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conferenceObject
Data de publicação
2016
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IEEE
Autores
MORETTI, Caio Benatti
JOAQUIM, Ricardo C.
MAZZOLENI, Stefano
CAURIN, Glauco A. P.
Citação
2016 6TH IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB), p.717-722, 2016
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Aiming to perform an extraction of features which are strongly related to hemiparesis, this work describes a case study involving the efforts of patients in upper-limb rehabilitation, diagnosed with such pathology. Expressed as data (kinematic and dynamic measures), patients' performance were sensed and stored by a single InMotion Arm robotic device for further analysis. It was applied a Knowledge Discovery roadmap over collected data in order to preprocess, transform and perform data mining through machine learning methods. Our efforts culminated in a pattern classification with the abilty to distinguish hemiparetic sides with an accuracy rate of 94%, having 8 features of rehabilitation performance feeding the input. Interpreting the obtained feature structure, it was observed that force-related attributes are more significant to the composition of the extracted pattern.
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Referências
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